Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations3377
Missing cells7026
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory629.7 B

Variable types

Text4
Categorical10
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 4 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 3 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
floorNum is highly overall correlated with property_typeHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 3 other fieldsHigh correlation
servant room is highly overall correlated with super_built_up_areaHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (53.3%)Imbalance
facing has 918 (27.2%) missing valuesMissing
property_id has 918 (27.2%) missing valuesMissing
super_built_up_area has 1673 (49.5%) missing valuesMissing
built_up_area has 1712 (50.7%) missing valuesMissing
carpet_area has 1732 (51.3%) missing valuesMissing
area is highly skewed (γ1 = 30.28779746)Skewed
carpet_area is highly skewed (γ1 = 22.88577403)Skewed
floorNum has 114 (3.4%) zerosZeros
luxury_score has 457 (13.5%) zerosZeros

Reproduction

Analysis started2024-09-17 12:58:12.675309
Analysis finished2024-09-17 12:58:44.847936
Duration32.17 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct591
Distinct (%)17.5%
Missing1
Missing (%)< 0.1%
Memory size242.7 KiB
2024-09-17T18:28:46.364468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length49
Median length38
Mean length16.589159
Min length2

Characters and Unicode

Total characters56005
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique258 ?
Unique (%)7.6%

Sample

1st rowemaar palm gardens
2nd roweros wembley estate
3rd rowss the leaf
4th rowinternational city by sobha phase 2
5th rowss the coralwood
ValueCountFrequency (%)
independent 539
 
6.2%
the 313
 
3.6%
dlf 192
 
2.2%
city 162
 
1.9%
emaar 155
 
1.8%
m3m 152
 
1.8%
park 130
 
1.5%
heights 123
 
1.4%
signature 118
 
1.4%
global 107
 
1.2%
Other values (715) 6676
77.0%
2024-09-17T18:28:47.684409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6137
 
11.0%
5292
 
9.4%
a 5252
 
9.4%
n 4038
 
7.2%
r 3679
 
6.6%
i 3611
 
6.4%
t 3428
 
6.1%
s 3054
 
5.5%
l 2483
 
4.4%
d 2430
 
4.3%
Other values (30) 16601
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50181
89.6%
Space Separator 5292
 
9.4%
Decimal Number 514
 
0.9%
Dash Punctuation 11
 
< 0.1%
Other Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6137
12.2%
a 5252
 
10.5%
n 4038
 
8.0%
r 3679
 
7.3%
i 3611
 
7.2%
t 3428
 
6.8%
s 3054
 
6.1%
l 2483
 
4.9%
d 2430
 
4.8%
o 2429
 
4.8%
Other values (16) 13640
27.2%
Decimal Number
ValueCountFrequency (%)
3 200
38.9%
2 80
 
15.6%
1 74
 
14.4%
6 54
 
10.5%
8 31
 
6.0%
4 19
 
3.7%
5 17
 
3.3%
0 14
 
2.7%
9 13
 
2.5%
7 12
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 5
71.4%
/ 2
 
28.6%
Space Separator
ValueCountFrequency (%)
5292
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50181
89.6%
Common 5824
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6137
12.2%
a 5252
 
10.5%
n 4038
 
8.0%
r 3679
 
7.3%
i 3611
 
7.2%
t 3428
 
6.8%
s 3054
 
6.1%
l 2483
 
4.9%
d 2430
 
4.8%
o 2429
 
4.8%
Other values (16) 13640
27.2%
Common
ValueCountFrequency (%)
5292
90.9%
3 200
 
3.4%
2 80
 
1.4%
1 74
 
1.3%
6 54
 
0.9%
8 31
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 14
 
0.2%
9 13
 
0.2%
Other values (4) 30
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6137
 
11.0%
5292
 
9.4%
a 5252
 
9.4%
n 4038
 
7.2%
r 3679
 
6.6%
i 3611
 
6.4%
t 3428
 
6.1%
s 3054
 
5.5%
l 2483
 
4.4%
d 2430
 
4.3%
Other values (30) 16601
29.6%

property_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size202.1 KiB
flat
2459 
house
918 

Length

Max length5
Median length4
Mean length4.2718389
Min length4

Characters and Unicode

Total characters14426
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowhouse
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2459
72.8%
house 918
 
27.2%

Length

2024-09-17T18:28:47.999952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:28:48.276910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
flat 2459
72.8%
house 918
 
27.2%

Most occurring characters

ValueCountFrequency (%)
f 2459
17.0%
l 2459
17.0%
a 2459
17.0%
t 2459
17.0%
h 918
 
6.4%
o 918
 
6.4%
u 918
 
6.4%
s 918
 
6.4%
e 918
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14426
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2459
17.0%
l 2459
17.0%
a 2459
17.0%
t 2459
17.0%
h 918
 
6.4%
o 918
 
6.4%
u 918
 
6.4%
s 918
 
6.4%
e 918
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 14426
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2459
17.0%
l 2459
17.0%
a 2459
17.0%
t 2459
17.0%
h 918
 
6.4%
o 918
 
6.4%
u 918
 
6.4%
s 918
 
6.4%
e 918
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2459
17.0%
l 2459
17.0%
a 2459
17.0%
t 2459
17.0%
h 918
 
6.4%
o 918
 
6.4%
u 918
 
6.4%
s 918
 
6.4%
e 918
 
6.4%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)13.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.6345089
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:28:48.555582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.41
Q10.99
median1.6
Q32.8625
95-th percentile9
Maximum31.5
Range31.43
Interquartile range (IQR)1.8725

Descriptive statistics

Standard deviation3.0673945
Coefficient of variation (CV)1.1643136
Kurtosis14.078755
Mean2.6345089
Median Absolute Deviation (MAD)0.75
Skewness3.2084737
Sum8851.95
Variance9.4089089
MonotonicityNot monotonic
2024-09-17T18:28:48.887069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 79
 
2.3%
1.1 62
 
1.8%
1.2 57
 
1.7%
1.5 57
 
1.7%
0.9 57
 
1.7%
1.4 56
 
1.7%
1.3 51
 
1.5%
2 50
 
1.5%
1.75 46
 
1.4%
1.6 45
 
1.3%
Other values (457) 2800
82.9%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 4
0.1%
0.21 3
0.1%
0.22 4
0.1%
0.23 1
 
< 0.1%
0.24 3
0.1%
0.25 5
0.1%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct2455
Distinct (%)73.1%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean14568.471
Minimum5
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:28:49.216972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile4854
Q17004.75
median9166
Q314170.25
95-th percentile34853.7
Maximum600000
Range599995
Interquartile range (IQR)7165.5

Descriptive statistics

Standard deviation24477.881
Coefficient of variation (CV)1.6801957
Kurtosis163.58021
Mean14568.471
Median Absolute Deviation (MAD)2797
Skewness10.655408
Sum48950062
Variance5.9916668 × 108
MonotonicityNot monotonic
2024-09-17T18:28:49.555415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 26
 
0.8%
8000 20
 
0.6%
5000 16
 
0.5%
11111 14
 
0.4%
8333 13
 
0.4%
22222 13
 
0.4%
6666 12
 
0.4%
33333 11
 
0.3%
7500 11
 
0.3%
6000 11
 
0.3%
Other values (2445) 3213
95.1%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
151 1
< 0.1%
232 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%
Distinct2192
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Memory size368.2 KiB
2024-09-17T18:28:50.402660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.642582
Min length12

Characters and Unicode

Total characters184528
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1743 ?
Unique (%)51.6%

Sample

1st rowSuper Built up area 1900(176.52 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1240 sq.ft. (115.2 sq.m.)
2nd rowSuper Built up area 1376(127.83 sq.m.)
3rd rowSuper Built up area 2812(261.24 sq.m.)Built Up area: 2600 sq.ft. (241.55 sq.m.)Carpet area: 2400 sq.ft. (222.97 sq.m.)
4th rowPlot area 692(578.6 sq.m.)
5th rowSuper Built up area 1750(162.58 sq.m.)
ValueCountFrequency (%)
area 5161
18.6%
sq.m 3360
12.1%
up 2788
 
10.0%
built 2116
 
7.6%
super 1704
 
6.1%
sq.ft 1628
 
5.9%
sq.m.)carpet 1108
 
4.0%
plot 728
 
2.6%
sq.m.)built 670
 
2.4%
carpet 534
 
1.9%
Other values (2655) 8024
28.8%
2024-09-17T18:28:51.658026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24444
 
13.2%
. 18915
 
10.3%
a 12117
 
6.6%
r 8660
 
4.7%
1 8632
 
4.7%
e 8510
 
4.6%
s 7068
 
3.8%
q 6918
 
3.7%
t 6789
 
3.7%
0 6243
 
3.4%
Other values (25) 76232
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76495
41.5%
Decimal Number 43728
23.7%
Space Separator 24444
 
13.2%
Other Punctuation 21644
 
11.7%
Uppercase Letter 7949
 
4.3%
Close Punctuation 5134
 
2.8%
Open Punctuation 5134
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12117
15.8%
r 8660
11.3%
e 8510
11.1%
s 7068
9.2%
q 6918
9.0%
t 6789
8.9%
u 6196
8.1%
p 6137
8.0%
m 5140
6.7%
l 3516
 
4.6%
Other values (5) 5444
7.1%
Decimal Number
ValueCountFrequency (%)
1 8632
19.7%
0 6243
14.3%
2 5338
12.2%
5 4315
9.9%
3 3666
8.4%
4 3393
 
7.8%
6 3379
 
7.7%
8 2938
 
6.7%
7 2931
 
6.7%
9 2893
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
B 2788
35.1%
S 1704
21.4%
C 1645
20.7%
U 1084
 
13.6%
P 728
 
9.2%
Other Punctuation
ValueCountFrequency (%)
. 18915
87.4%
: 2729
 
12.6%
Space Separator
ValueCountFrequency (%)
24444
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5134
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100084
54.2%
Latin 84444
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12117
14.3%
r 8660
10.3%
e 8510
10.1%
s 7068
8.4%
q 6918
8.2%
t 6789
8.0%
u 6196
7.3%
p 6137
7.3%
m 5140
 
6.1%
l 3516
 
4.2%
Other values (10) 13393
15.9%
Common
ValueCountFrequency (%)
24444
24.4%
. 18915
18.9%
1 8632
 
8.6%
0 6243
 
6.2%
2 5338
 
5.3%
) 5134
 
5.1%
( 5134
 
5.1%
5 4315
 
4.3%
3 3666
 
3.7%
4 3393
 
3.4%
Other values (5) 14870
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 184528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24444
 
13.2%
. 18915
 
10.3%
a 12117
 
6.6%
r 8660
 
4.7%
1 8632
 
4.7%
e 8510
 
4.6%
s 7068
 
3.8%
q 6918
 
3.7%
t 6789
 
3.7%
0 6243
 
3.4%
Other values (25) 76232
41.3%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1233
Distinct (%)36.7%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2757.4863
Minimum45
Maximum642857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:28:51.989994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile515.9
Q11266.75
median1757
Q32343.25
95-th percentile4337
Maximum642857
Range642812
Interquartile range (IQR)1076.5

Descriptive statistics

Standard deviation18924.998
Coefficient of variation (CV)6.8631339
Kurtosis965.13016
Mean2757.4863
Median Absolute Deviation (MAD)527
Skewness30.287797
Sum9265154
Variance3.5815554 × 108
MonotonicityNot monotonic
2024-09-17T18:28:52.348796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 52
 
1.5%
1800 47
 
1.4%
1350 45
 
1.3%
3240 45
 
1.3%
1950 43
 
1.3%
900 41
 
1.2%
2700 40
 
1.2%
2000 34
 
1.0%
2250 24
 
0.7%
2150 22
 
0.7%
Other values (1223) 2967
87.9%
ValueCountFrequency (%)
45 1
 
< 0.1%
50 5
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
 
0.1%
61 1
 
< 0.1%
67 2
 
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
ValueCountFrequency (%)
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
55000 1
< 0.1%

bedRoom
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4791235
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:28:52.633547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile7
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1035669
Coefficient of variation (CV)0.60462553
Kurtosis41.798468
Mean3.4791235
Median Absolute Deviation (MAD)1
Skewness4.682147
Sum11749
Variance4.4249935
MonotonicityNot monotonic
2024-09-17T18:28:52.959925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 1379
40.8%
2 779
23.1%
4 643
19.0%
5 213
 
6.3%
1 106
 
3.1%
6 79
 
2.3%
9 43
 
1.3%
8 31
 
0.9%
7 29
 
0.9%
12 27
 
0.8%
Other values (11) 48
 
1.4%
ValueCountFrequency (%)
1 106
 
3.1%
2 779
23.1%
3 1379
40.8%
4 643
19.0%
5 213
 
6.3%
6 79
 
2.3%
7 29
 
0.9%
8 31
 
0.9%
9 43
 
1.3%
10 21
 
0.6%
ValueCountFrequency (%)
36 1
 
< 0.1%
34 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.4%
14 1
 
< 0.1%
13 4
 
0.1%
12 27
0.8%

bathroom
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5356826
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:28:53.239919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile7
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1436801
Coefficient of variation (CV)0.6062988
Kurtosis39.468022
Mean3.5356826
Median Absolute Deviation (MAD)1
Skewness4.4458021
Sum11940
Variance4.5953644
MonotonicityNot monotonic
2024-09-17T18:28:53.546008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 1017
30.1%
2 870
25.8%
4 779
23.1%
5 286
 
8.5%
1 136
 
4.0%
6 117
 
3.5%
9 42
 
1.2%
7 41
 
1.2%
8 25
 
0.7%
12 22
 
0.7%
Other values (11) 42
 
1.2%
ValueCountFrequency (%)
1 136
 
4.0%
2 870
25.8%
3 1017
30.1%
4 779
23.1%
5 286
 
8.5%
6 117
 
3.5%
7 41
 
1.2%
8 25
 
0.7%
9 42
 
1.2%
10 11
 
0.3%
ValueCountFrequency (%)
36 1
 
< 0.1%
34 1
 
< 0.1%
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.7%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size192.3 KiB
3+
1086 
3
1008 
2
796 
1
308 
0
179 

Length

Max length2
Median length1
Mean length1.3215872
Min length1

Characters and Unicode

Total characters4463
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row3+
5th row2

Common Values

ValueCountFrequency (%)
3+ 1086
32.2%
3 1008
29.8%
2 796
23.6%
1 308
 
9.1%
0 179
 
5.3%

Length

2024-09-17T18:28:53.867291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:28:54.141105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 2094
62.0%
2 796
 
23.6%
1 308
 
9.1%
0 179
 
5.3%

Most occurring characters

ValueCountFrequency (%)
3 2094
46.9%
+ 1086
24.3%
2 796
 
17.8%
1 308
 
6.9%
0 179
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3377
75.7%
Math Symbol 1086
 
24.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2094
62.0%
2 796
 
23.6%
1 308
 
9.1%
0 179
 
5.3%
Math Symbol
ValueCountFrequency (%)
+ 1086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2094
46.9%
+ 1086
24.3%
2 796
 
17.8%
1 308
 
6.9%
0 179
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2094
46.9%
+ 1086
24.3%
2 796
 
17.8%
1 308
 
6.9%
0 179
 
4.0%

floorNum
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)1.3%
Missing21
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean6.7646007
Minimum0
Maximum51
Zeros114
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:28:54.467172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0632541
Coefficient of variation (CV)0.89632107
Kurtosis4.773865
Mean6.7646007
Median Absolute Deviation (MAD)3
Skewness1.7519357
Sum22702
Variance36.763051
MonotonicityNot monotonic
2024-09-17T18:28:54.852441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2 480
14.2%
3 465
13.8%
1 319
 
9.4%
4 288
 
8.5%
8 179
 
5.3%
10 165
 
4.9%
6 163
 
4.8%
7 160
 
4.7%
5 149
 
4.4%
9 146
 
4.3%
Other values (33) 842
24.9%
ValueCountFrequency (%)
0 114
 
3.4%
1 319
9.4%
2 480
14.2%
3 465
13.8%
4 288
8.5%
5 149
 
4.4%
6 163
 
4.8%
7 160
 
4.7%
8 179
 
5.3%
9 146
 
4.3%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

MISSING 

Distinct8
Distinct (%)0.3%
Missing918
Missing (%)27.2%
Memory size210.7 KiB
North-East
583 
East
576 
North
360 
West
234 
South
220 
Other values (3)
486 

Length

Max length10
Median length5
Mean length6.8442456
Min length4

Characters and Unicode

Total characters16830
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowEast
3rd rowNorth
4th rowNorth-East
5th rowNorth

Common Values

ValueCountFrequency (%)
North-East 583
17.3%
East 576
17.1%
North 360
 
10.7%
West 234
 
6.9%
South 220
 
6.5%
North-West 178
 
5.3%
South-East 166
 
4.9%
South-West 142
 
4.2%
(Missing) 918
27.2%

Length

2024-09-17T18:28:55.151064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:28:55.416684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
north-east 583
23.7%
east 576
23.4%
north 360
14.6%
west 234
9.5%
south 220
 
8.9%
north-west 178
 
7.2%
south-east 166
 
6.8%
south-west 142
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3528
21.0%
s 1879
11.2%
o 1649
9.8%
h 1649
9.8%
E 1325
 
7.9%
a 1325
 
7.9%
N 1121
 
6.7%
r 1121
 
6.7%
- 1069
 
6.4%
W 554
 
3.3%
Other values (3) 1610
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12233
72.7%
Uppercase Letter 3528
 
21.0%
Dash Punctuation 1069
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3528
28.8%
s 1879
15.4%
o 1649
13.5%
h 1649
13.5%
a 1325
 
10.8%
r 1121
 
9.2%
e 554
 
4.5%
u 528
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1325
37.6%
N 1121
31.8%
W 554
15.7%
S 528
 
15.0%
Dash Punctuation
ValueCountFrequency (%)
- 1069
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15761
93.6%
Common 1069
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3528
22.4%
s 1879
11.9%
o 1649
10.5%
h 1649
10.5%
E 1325
 
8.4%
a 1325
 
8.4%
N 1121
 
7.1%
r 1121
 
7.1%
W 554
 
3.5%
e 554
 
3.5%
Other values (2) 1056
 
6.7%
Common
ValueCountFrequency (%)
- 1069
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3528
21.0%
s 1879
11.2%
o 1649
9.8%
h 1649
9.8%
E 1325
 
7.9%
a 1325
 
7.9%
N 1121
 
6.7%
r 1121
 
6.7%
- 1069
 
6.4%
W 554
 
3.3%
Other values (3) 1610
9.6%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size232.0 KiB
Relatively New
1543 
New Property
514 
Moderately Old
507 
Undefined
305 
Old Property
288 

Length

Max length18
Median length14
Mean length13.334024
Min length9

Characters and Unicode

Total characters45029
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowRelatively New
3rd rowRelatively New
4th rowRelatively New
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1543
45.7%
New Property 514
 
15.2%
Moderately Old 507
 
15.0%
Undefined 305
 
9.0%
Old Property 288
 
8.5%
Under Construction 220
 
6.5%

Length

2024-09-17T18:28:55.736719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:28:56.048723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
new 2057
31.9%
relatively 1543
23.9%
property 802
 
12.4%
old 795
 
12.3%
moderately 507
 
7.9%
undefined 305
 
4.7%
under 220
 
3.4%
construction 220
 
3.4%

Most occurring characters

ValueCountFrequency (%)
e 7789
17.3%
l 4388
 
9.7%
t 3292
 
7.3%
3072
 
6.8%
y 2852
 
6.3%
r 2551
 
5.7%
d 2132
 
4.7%
i 2068
 
4.6%
N 2057
 
4.6%
w 2057
 
4.6%
Other values (15) 12771
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35508
78.9%
Uppercase Letter 6449
 
14.3%
Space Separator 3072
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7789
21.9%
l 4388
12.4%
t 3292
9.3%
y 2852
 
8.0%
r 2551
 
7.2%
d 2132
 
6.0%
i 2068
 
5.8%
w 2057
 
5.8%
a 2050
 
5.8%
o 1749
 
4.9%
Other values (7) 4580
12.9%
Uppercase Letter
ValueCountFrequency (%)
N 2057
31.9%
R 1543
23.9%
P 802
 
12.4%
O 795
 
12.3%
U 525
 
8.1%
M 507
 
7.9%
C 220
 
3.4%
Space Separator
ValueCountFrequency (%)
3072
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41957
93.2%
Common 3072
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7789
18.6%
l 4388
10.5%
t 3292
 
7.8%
y 2852
 
6.8%
r 2551
 
6.1%
d 2132
 
5.1%
i 2068
 
4.9%
N 2057
 
4.9%
w 2057
 
4.9%
a 2050
 
4.9%
Other values (14) 10721
25.6%
Common
ValueCountFrequency (%)
3072
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7789
17.3%
l 4388
 
9.7%
t 3292
 
7.3%
3072
 
6.8%
y 2852
 
6.3%
r 2551
 
5.7%
d 2132
 
4.7%
i 2068
 
4.6%
N 2057
 
4.6%
w 2057
 
4.6%
Other values (15) 12771
28.4%

property_id
Text

MISSING 

Distinct2455
Distinct (%)99.8%
Missing918
Missing (%)27.2%
Memory size194.3 KiB
2024-09-17T18:28:56.929311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.99878
Min length8

Characters and Unicode

Total characters22128
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2451 ?
Unique (%)99.7%

Sample

1st rowV69525180
2nd rowK69325724
3rd rowK70087544
4th rowH66415758
5th rowV69824946
ValueCountFrequency (%)
g66828720 2
 
0.1%
k69091354 2
 
0.1%
m62958574 2
 
0.1%
c69153300 2
 
0.1%
t69179276 1
 
< 0.1%
x69844094 1
 
< 0.1%
t68022426 1
 
< 0.1%
k70087544 1
 
< 0.1%
h66415758 1
 
< 0.1%
v69824946 1
 
< 0.1%
Other values (2445) 2445
99.4%
2024-09-17T18:28:58.226761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 3725
16.8%
9 2459
11.1%
8 2162
9.8%
0 2067
9.3%
4 1836
8.3%
2 1791
8.1%
7 1612
7.3%
5 1376
 
6.2%
1 1351
 
6.1%
3 1290
 
5.8%
Other values (26) 2459
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19669
88.9%
Uppercase Letter 2459
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 108
 
4.4%
G 108
 
4.4%
U 107
 
4.4%
L 107
 
4.4%
J 105
 
4.3%
I 105
 
4.3%
O 103
 
4.2%
Y 99
 
4.0%
B 99
 
4.0%
P 98
 
4.0%
Other values (16) 1420
57.7%
Decimal Number
ValueCountFrequency (%)
6 3725
18.9%
9 2459
12.5%
8 2162
11.0%
0 2067
10.5%
4 1836
9.3%
2 1791
9.1%
7 1612
8.2%
5 1376
 
7.0%
1 1351
 
6.9%
3 1290
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 19669
88.9%
Latin 2459
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 108
 
4.4%
G 108
 
4.4%
U 107
 
4.4%
L 107
 
4.4%
J 105
 
4.3%
I 105
 
4.3%
O 103
 
4.2%
Y 99
 
4.0%
B 99
 
4.0%
P 98
 
4.0%
Other values (16) 1420
57.7%
Common
ValueCountFrequency (%)
6 3725
18.9%
9 2459
12.5%
8 2162
11.0%
0 2067
10.5%
4 1836
9.3%
2 1791
9.1%
7 1612
8.2%
5 1376
 
7.0%
1 1351
 
6.9%
3 1290
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 3725
16.8%
9 2459
11.1%
8 2162
9.8%
0 2067
9.3%
4 1836
8.3%
2 1791
8.1%
7 1612
7.3%
5 1376
 
6.2%
1 1351
 
6.1%
3 1290
 
5.8%
Other values (26) 2459
11.1%

sector
Text

Distinct108
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
2024-09-17T18:28:58.836119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length27
Median length9
Mean length9.2312704
Min length5

Characters and Unicode

Total characters31174
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsector 83
2nd rowsector 50
3rd rowsector 85
4th rowsector 109
5th rowsector 84
ValueCountFrequency (%)
sector 3345
49.2%
85 108
 
1.6%
102 108
 
1.6%
70 104
 
1.5%
92 98
 
1.4%
69 93
 
1.4%
90 88
 
1.3%
65 87
 
1.3%
81 87
 
1.3%
109 86
 
1.3%
Other values (105) 2595
38.2%
2024-09-17T18:28:59.776234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3422
11.0%
c 3418
11.0%
s 3402
10.9%
o 3380
10.8%
r 3364
10.8%
e 3362
10.8%
t 3359
10.8%
1 1143
 
3.7%
0 825
 
2.6%
9 767
 
2.5%
Other values (21) 4732
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20658
66.3%
Decimal Number 7094
 
22.8%
Space Separator 3422
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 3418
16.5%
s 3402
16.5%
o 3380
16.4%
r 3364
16.3%
e 3362
16.3%
t 3359
16.3%
a 139
 
0.7%
h 47
 
0.2%
l 31
 
0.2%
k 28
 
0.1%
Other values (10) 128
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 1143
16.1%
0 825
11.6%
9 767
10.8%
8 754
10.6%
6 747
10.5%
7 682
9.6%
5 603
8.5%
2 601
8.5%
3 532
7.5%
4 440
 
6.2%
Space Separator
ValueCountFrequency (%)
3422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20658
66.3%
Common 10516
33.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 3418
16.5%
s 3402
16.5%
o 3380
16.4%
r 3364
16.3%
e 3362
16.3%
t 3359
16.3%
a 139
 
0.7%
h 47
 
0.2%
l 31
 
0.2%
k 28
 
0.1%
Other values (10) 128
 
0.6%
Common
ValueCountFrequency (%)
3422
32.5%
1 1143
 
10.9%
0 825
 
7.8%
9 767
 
7.3%
8 754
 
7.2%
6 747
 
7.1%
7 682
 
6.5%
5 603
 
5.7%
2 601
 
5.7%
3 532
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3422
11.0%
c 3418
11.0%
s 3402
10.9%
o 3380
10.8%
r 3364
10.8%
e 3362
10.8%
t 3359
10.8%
1 1143
 
3.7%
0 825
 
2.6%
9 767
 
2.5%
Other values (21) 4732
15.2%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct542
Distinct (%)31.8%
Missing1673
Missing (%)49.5%
Infinite0
Infinite (%)0.0%
Mean1941.2231
Minimum89
Maximum6926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:29:00.112432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile916.5
Q11534
median1867.5
Q32250
95-th percentile3156
Maximum6926
Range6837
Interquartile range (IQR)716

Descriptive statistics

Standard deviation708.13135
Coefficient of variation (CV)0.36478617
Kurtosis3.9276263
Mean1941.2231
Median Absolute Deviation (MAD)349.5
Skewness1.1981879
Sum3307844.2
Variance501450.01
MonotonicityNot monotonic
2024-09-17T18:29:00.508207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.1%
1650 36
 
1.1%
1578 25
 
0.7%
2000 24
 
0.7%
2150 21
 
0.6%
1640 20
 
0.6%
1900 19
 
0.6%
2408 19
 
0.6%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (532) 1468
43.5%
(Missing) 1673
49.5%
ValueCountFrequency (%)
89 1
< 0.1%
161 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
417 1
< 0.1%
431 1
< 0.1%
ValueCountFrequency (%)
6926 1
 
< 0.1%
5514 1
 
< 0.1%
5200 1
 
< 0.1%
4890 1
 
< 0.1%
4857 1
 
< 0.1%
4848 2
0.1%
4739 3
0.1%
4690 1
 
< 0.1%
4650 1
 
< 0.1%
4632 1
 
< 0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct629
Distinct (%)37.8%
Missing1712
Missing (%)50.7%
Infinite0
Infinite (%)0.0%
Mean1949.0776
Minimum2
Maximum26000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:29:00.872515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile225
Q11080
median1635
Q32398
95-th percentile4696
Maximum26000
Range25998
Interquartile range (IQR)1318

Descriptive statistics

Standard deviation1587.399
Coefficient of variation (CV)0.81443603
Kurtosis36.330834
Mean1949.0776
Median Absolute Deviation (MAD)654
Skewness3.750232
Sum3245214.2
Variance2519835.7
MonotonicityNot monotonic
2024-09-17T18:29:01.263128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.2%
3240 38
 
1.1%
900 34
 
1.0%
2700 33
 
1.0%
1900 33
 
1.0%
1350 31
 
0.9%
1600 25
 
0.7%
1300 23
 
0.7%
2000 22
 
0.7%
1700 20
 
0.6%
Other values (619) 1365
40.4%
(Missing) 1712
50.7%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
45 1
 
< 0.1%
50 4
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
ValueCountFrequency (%)
26000 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8260 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct662
Distinct (%)40.2%
Missing1732
Missing (%)51.3%
Infinite0
Infinite (%)0.0%
Mean2692.0976
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:29:01.678521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile348.4
Q1913
median1330
Q31800
95-th percentile3000
Maximum607936
Range607921
Interquartile range (IQR)887

Descriptive statistics

Standard deviation24288.542
Coefficient of variation (CV)9.0221626
Kurtosis533.4792
Mean2692.0976
Median Absolute Deviation (MAD)436
Skewness22.885774
Sum4428500.5
Variance5.8993327 × 108
MonotonicityNot monotonic
2024-09-17T18:29:02.029037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 41
 
1.2%
1800 32
 
0.9%
1600 31
 
0.9%
1200 31
 
0.9%
1500 28
 
0.8%
1350 26
 
0.8%
1650 26
 
0.8%
1450 21
 
0.6%
2000 20
 
0.6%
1000 19
 
0.6%
Other values (652) 1370
40.6%
(Missing) 1732
51.3%
ValueCountFrequency (%)
15 1
< 0.1%
33 1
< 0.1%
48 1
< 0.1%
50 1
< 0.1%
59 1
< 0.1%
60 1
< 0.1%
72 1
< 0.1%
77.31 1
< 0.1%
84.01 1
< 0.1%
85 1
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%
18122 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size191.3 KiB
0
2711 
1
666 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3377
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2711
80.3%
1 666
 
19.7%

Length

2024-09-17T18:29:02.347857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:29:02.555687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2711
80.3%
1 666
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 2711
80.3%
1 666
 
19.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3377
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2711
80.3%
1 666
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2711
80.3%
1 666
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2711
80.3%
1 666
 
19.7%

servant room
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size191.3 KiB
0
2113 
1
1264 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3377
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2113
62.6%
1 1264
37.4%

Length

2024-09-17T18:29:02.797159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:29:03.015899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2113
62.6%
1 1264
37.4%

Most occurring characters

ValueCountFrequency (%)
0 2113
62.6%
1 1264
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3377
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2113
62.6%
1 1264
37.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2113
62.6%
1 1264
37.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2113
62.6%
1 1264
37.4%

store room
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size191.3 KiB
0
3041 
1
336 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3377
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3041
90.1%
1 336
 
9.9%

Length

2024-09-17T18:29:03.250268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:29:03.469013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3041
90.1%
1 336
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 3041
90.1%
1 336
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3377
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3041
90.1%
1 336
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 3377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3041
90.1%
1 336
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3041
90.1%
1 336
 
9.9%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size191.3 KiB
0
2741 
1
636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3377
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2741
81.2%
1 636
 
18.8%

Length

2024-09-17T18:29:03.705940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:29:03.940310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2741
81.2%
1 636
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 2741
81.2%
1 636
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3377
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2741
81.2%
1 636
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2741
81.2%
1 636
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2741
81.2%
1 636
 
18.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size191.3 KiB
0
2985 
1
392 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3377
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2985
88.4%
1 392
 
11.6%

Length

2024-09-17T18:29:04.159054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:29:04.389881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2985
88.4%
1 392
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 2985
88.4%
1 392
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3377
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2985
88.4%
1 392
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2985
88.4%
1 392
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2985
88.4%
1 392
 
11.6%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size191.3 KiB
0
2220 
2
970 
1
 
187

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3377
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2220
65.7%
2 970
28.7%
1 187
 
5.5%

Length

2024-09-17T18:29:04.639876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T18:29:04.863636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2220
65.7%
2 970
28.7%
1 187
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2220
65.7%
2 970
28.7%
1 187
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3377
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2220
65.7%
2 970
28.7%
1 187
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2220
65.7%
2 970
28.7%
1 187
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2220
65.7%
2 970
28.7%
1 187
 
5.5%

luxury_score
Real number (ℝ)

ZEROS 

Distinct161
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.047379
Minimum0
Maximum174
Zeros457
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2024-09-17T18:29:05.129255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129
median58
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)81

Descriptive statistics

Standard deviation53.713443
Coefficient of variation (CV)0.75602287
Kurtosis-0.91098632
Mean71.047379
Median Absolute Deviation (MAD)39
Skewness0.45482686
Sum239927
Variance2885.134
MonotonicityNot monotonic
2024-09-17T18:29:05.457371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 457
 
13.5%
49 329
 
9.7%
174 184
 
5.4%
165 52
 
1.5%
44 50
 
1.5%
60 47
 
1.4%
38 46
 
1.4%
72 44
 
1.3%
45 40
 
1.2%
15 40
 
1.2%
Other values (151) 2088
61.8%
ValueCountFrequency (%)
0 457
13.5%
5 6
 
0.2%
6 6
 
0.2%
7 38
 
1.1%
8 32
 
0.9%
9 9
 
0.3%
12 7
 
0.2%
13 10
 
0.3%
14 12
 
0.4%
15 40
 
1.2%
ValueCountFrequency (%)
174 184
5.4%
169 1
 
< 0.1%
168 7
 
0.2%
167 19
 
0.6%
166 10
 
0.3%
165 52
 
1.5%
161 2
 
0.1%
160 26
 
0.8%
159 21
 
0.6%
158 32
 
0.9%

Interactions

2024-09-17T18:28:40.196544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:18.145305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:20.653940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:22.984479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:25.348880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:27.714523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:29.985366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:32.233981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:35.187117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:37.847057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:40.424427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:18.474954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:20.885734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:23.234468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:25.567622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:27.921008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:30.188483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:32.503939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:35.491095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:38.050176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:40.658799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:18.709944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:21.104478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:23.531335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:25.804618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:28.155378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:30.407229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:32.939399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:35.733735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:38.300168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:40.898272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:18.928687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:21.338848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:23.752625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:26.023362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:28.389745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:30.651118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:33.204934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:35.985832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:38.518911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:41.129625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:19.147432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:21.588839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:24.002615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:26.242108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:28.629372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:30.861733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:33.500273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:36.231687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:38.772167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:41.383370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:19.366174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:21.810096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:24.221362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:26.476475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:28.857874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:31.080477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:33.767155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:36.472316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:39.028059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:41.612278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:19.743727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:22.028841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:24.455727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:26.696586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:29.076620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:31.283593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:34.018169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:36.736972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:39.257562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:41.857096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:19.978095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:22.278832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:24.697641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:26.946582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:29.310989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:31.502339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:34.280540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:36.925910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:39.486661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:42.090203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:20.196835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:22.501718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:24.895766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:27.149700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:29.514103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:31.741482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:34.595601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:37.402431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:39.697093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:42.326467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:20.415581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:22.734487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:25.114510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:27.384067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:29.750998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:31.998368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:34.897245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:37.605549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-17T18:28:39.929731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-17T18:29:05.805006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2790.0830.1300.0540.0000.0950.1470.2140.2660.1180.2000.1020.0620.4310.3010.1510.1510.107
area0.0001.0000.0190.6610.5810.8280.7730.0000.1180.0000.2720.0460.0180.7250.1520.0280.0060.0000.0210.944
balcony0.2790.0191.0000.1660.1130.1280.0380.0170.0830.1880.2280.0790.2000.1340.0370.2200.4390.1450.1790.334
bathroom0.0830.6610.1661.0000.8480.4450.5510.036-0.0140.1600.1800.0630.2600.7030.3880.4240.2990.1700.1490.812
bedRoom0.1300.5810.1130.8481.0000.3410.5180.038-0.1320.1370.0360.0640.2680.6520.3960.6050.1240.1950.1580.796
built_up_area0.0540.8280.1280.4450.3411.0000.9680.0250.1140.1130.3080.0890.2900.5940.0930.3030.3270.2520.2310.922
carpet_area0.0000.7730.0380.5510.5180.9681.0000.0000.1620.0000.2290.0110.0000.5620.0520.0000.0000.0000.0000.883
facing0.0950.0000.0170.0360.0380.0250.0001.0000.0000.0500.0670.0000.0130.0230.0000.1040.0000.0390.0000.019
floorNum0.1470.1180.083-0.014-0.1320.1140.1620.0001.0000.0000.2480.0410.108-0.019-0.1640.5310.0750.1200.0860.140
furnishing_type0.2140.0000.1880.1600.1370.1130.0000.0500.0001.0000.2520.0510.2260.1790.0160.0710.2700.1560.1430.111
luxury_score0.2660.2720.2280.1800.0360.3080.2290.0670.2480.2521.0000.1870.1820.2190.0320.3690.3620.2360.1900.200
others0.1180.0460.0790.0630.0640.0890.0110.0000.0410.0510.1871.0000.0150.0410.0310.0110.0260.1150.0220.065
pooja room0.2000.0180.2000.2600.2680.2900.0000.0130.1080.2260.1820.0151.0000.3330.0330.2460.2520.3090.3250.145
price0.1020.7250.1340.7030.6520.5940.5620.023-0.0190.1790.2190.0410.3331.0000.7190.5250.3480.3040.2470.760
price_per_sqft0.0620.1520.0370.3880.3960.0930.0520.000-0.1640.0160.0320.0310.0330.7191.0000.2030.0460.0000.0320.255
property_type0.4310.0280.2200.4240.6050.3030.0000.1040.5310.0710.3690.0110.2460.5250.2031.0000.0280.2310.1101.000
servant room0.3010.0060.4390.2990.1240.3270.0000.0000.0750.2700.3620.0260.2520.3480.0460.0281.0000.1540.1870.579
store room0.1510.0000.1450.1700.1950.2520.0000.0390.1200.1560.2360.1150.3090.3040.0000.2310.1541.0000.2260.044
study room0.1510.0210.1790.1490.1580.2310.0000.0000.0860.1430.1900.0220.3250.2470.0320.1100.1870.2261.0000.107
super_built_up_area0.1070.9440.3340.8120.7960.9220.8830.0190.1400.1110.2000.0650.1450.7600.2551.0000.5790.0440.1071.000

Missing values

2024-09-17T18:28:42.718739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-17T18:28:43.673543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-17T18:28:44.533040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

societyproperty_typepriceprice_per_sqftareaWithTypeareabedRoombathroombalconyfloorNumfacingagePossessionproperty_idsectorsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0emaar palm gardensflat1.759210.0Super Built up area 1900(176.52 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1240 sq.ft. (115.2 sq.m.)1900.03336.0WestRelatively NewV69525180sector 831900.01600.01240.0010002150
1eros wembley estateflat1.4010174.0Super Built up area 1376(127.83 sq.m.)1376.03329.0EastRelatively NewK69325724sector 501376.0NaNNaN001002117
2ss the leafflat2.137574.0Super Built up area 2812(261.24 sq.m.)Built Up area: 2600 sq.ft. (241.55 sq.m.)Carpet area: 2400 sq.ft. (222.97 sq.m.)2812.04436.0NorthRelatively NewK70087544sector 852812.02600.02400.000000049
3international city by sobha phase 2house6.2510035.0Plot area 692(578.6 sq.m.)6228.0573+3.0North-EastRelatively NewNaNsector 109NaN6228.0NaN111100147
4ss the coralwoodflat1.104857.0Super Built up area 1750(162.58 sq.m.)2265.03329.0NorthRelatively NewH66415758sector 841750.0NaNNaN00000075
5independenthouse2.0011111.0Plot area 200(167.23 sq.m.)1800.02212.0NaNRelatively NewNaNshivpuriNaN1800.0NaN0000000
6shapoorji pallonji joyville gurugramflat1.9920664.0Super Built up area 1349(125.33 sq.m.)Carpet area: 963 sq.ft. (89.47 sq.m.)963.022214.0SouthNew PropertyV69824946sector 1021349.0NaN963.0000000152
7independenthouse0.508333.0Plot area 600(55.74 sq.m.)600.03322.0EastModerately OldNaNsector 6NaN600.0NaN0001000
8tarc maceoflat0.896339.0Super Built up area 1404(130.44 sq.m.)Carpet area: 1200 sq.ft. (111.48 sq.m.)1404.0223+9.0EastRelatively NewG68726244sector 911404.0NaN1200.000001213
9paras dewsflat0.879255.0Super Built up area 1385(128.67 sq.m.)Built Up area: 1120 sq.ft. (104.05 sq.m.)Carpet area: 940 sq.ft. (87.33 sq.m.)940.0223+1.0North-EastNew PropertyX69767456sector 1061385.01120.0940.0000000174
societyproperty_typepriceprice_per_sqftareaWithTypeareabedRoombathroombalconyfloorNumfacingagePossessionproperty_idsectorsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3470sare homesflat1.125358.0Carpet area: 2090 (194.17 sq.m.)2090.044212.0North-WestRelatively NewS69810470sector 92NaNNaN2090.001000085
3471independenthouse7.0010000.0Plot area 350(32.52 sq.m.)Built Up area: 7000 sq.ft. (650.32 sq.m.)7000.010113+4.0South-EastRelatively NewNaNsector 39NaN7000.0NaN00010238
3472independenthouse5.5028424.0Plot area 215(179.77 sq.m.)1935.01101.0South-WestOld PropertyNaNsector 43NaN1935.0NaN00001012
3473row househouse0.439135.0Plot area 52(43.48 sq.m.)Built Up area: 1050 sq.yards (877.93 sq.m.)471.03332.0NaNRelatively NewNaNsector 105NaN1050.0NaN0000000
3474unitech uniworld resortshouse11.0026667.0Plot area 500(418.06 sq.m.)4125.05633.0NaNNew PropertyNaNsector 33NaN500.0NaN11000058
3475adani m2k oyster grandeflat4.5510000.0Super Built up area 4650(432 sq.m.)Built Up area: 4630 sq.ft. (430.14 sq.m.)Carpet area: 4550 sq.ft. (422.71 sq.m.)4550.0443+11.0North-EastRelatively NewV70001746sector 1024650.04630.04550.000000260
3476signature global soleraflat0.257862.0Built Up area: 318 (29.54 sq.m.)318.01100.0NaNUndefinedV69417148sector 107NaN318.0NaN00000055
3477housing board colonyhouse5.5030556.0Plot area 200(167.23 sq.m.)1800.04332.0NorthOld PropertyNaNsector 28NaN1800.0NaN00011222
3478bestech park view grand spaflat4.706786.0Super Built up area 6926(643.45 sq.m.)6926.0443+19.0NorthRelatively NewR61758448sector 816926.0NaNNaN010002140
3479la vida by tata housingflat1.3210344.0Super Built up area 1276(118.54 sq.m.)Built Up area: 1199 sq.ft. (111.39 sq.m.)Carpet area: 1099 sq.ft. (102.1 sq.m.)1276.02223.0North-EastRelatively NewI69730598sector 1131276.01199.01099.000000049